The effectiveness of the Ethics Quarter intervention on the ethical activity profile of nurse managers: A randomized controlled trial
Bibliographic record
Abstract
AIM: To test the effectiveness of a new ethics educational e-learning intervention, Ethics Quarter, in supporting nurse managers' ethical activity profile. BACKGROUND: Health care organisations need evidence-based ethics interventions to support nurse managers' ethical activity profile. METHODS: A parallel-group, individually randomized controlled trial was conducted in 2020. Finnish nurse managers nationwide [members of the Union of Health and Social Care Professionals in Finland (Tehy) trade union] were randomly allocated to intervention (n = 169) or control group (n = 172). The intervention group participated in the Ethics Quarter comprising twelve 15-min evidence-based educational 'quarters' spread over 6 weeks. The control group had standard organisational ethics structures. The primary and secondary outcomes were ethical activity profile and ethics knowledge, respectively. The Consolidated Standards of Reporting Trials (CONSORT) statement for study design and reporting was adopted. RESULTS: Ethical activity profile showed statistically significant differences in mean changes between the groups from baseline to 10 weeks: all five dimensions were statistically significantly higher in the intervention group compared with the control group (p = <.0001). CONCLUSION: The Ethics Quarter was effective in increasing nurse managers' ethical activity profile. IMPLICATIONS FOR NURSING MANAGEMENT: Applying this ethics educational e-learning intervention would benefit nursing management education and health care organisations. TRIAL REGISTRATION: clinicaltrials.gov: NCT04234503.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".